Because the proposed FG-LSTM is a variant based on the classical LSTM [1]}, we choose the classical LSTM and three other excellent variants including BI-LSTM, Phase LSTM [2]} and HE-LSTM [3]}. Recently, Transformer architecture has achieved the best performance in many problems, so we discuss the ability of Transformer related architecture to deal with multimodal irregular time-series. We chose the vanilla Transformer [4]} and further select one of excellent variants in it called Informer [5]}. Because our experiment does not involve the generation process, therefore, only the encoder part of the Transformer architecture is used, and get the final output directly through a fully connected feed-forward network. For LSTM related architectures, only use one layer. For Informer, the number of layers in the original author's open source code is selected, that is, \(n = 2\) . For Transformer, in order to better compare with Informer, we selecte the same encoder layers as Informer. In addition, \(d_{model}\)  is changed to 256, which is consistent with LSTM architecture, and there is no change in the parameter settings of Transformer related architecture.
